基于极限学习机的供水管网故障智能诊断方法  

Intelligent fault diagnosis of water supply network based on ELM

作　　者：高相铭[1] 刘付斌[1] 杨世凤[2] 

机构地区：[1]安阳师范学院物理与电气工程学院,河南安阳455000 [2]天津科技大学自动化学院,天津300222

出　　处：《计算机工程与设计》2013年第8期2887-2891,共5页Computer Engineering and Design

基　　金：国家"十一五"科技支撑计划重点基金项目(2006BAJ16B08)

摘　　要：为进一步提高传统极限学习机的泛化能力,提出了一种基于人工蜂群算法优化的极限学习机模型。该模型将人工蜂群算法的全局寻优能力和极限学习机的快速学习能力相结合,有效克服了传统极限学习机的过拟合现象。在确定水压变化比值作为故障特征参数的基础上,将优化后的极限学习机模型应用于供水管网的泄漏故障诊断实验,实验结果表明,经人工蜂群算法优化的极限学习机模型在故障诊断速度和精度方面均优于其他3种模型。To improve the generalization performance of the traditional extreme learning machine,an extreme learning machine model optimized is presented by artificial bee colony algorithm.The global optimization ability of artificial bee colony algorithm is combined with quick learning ability of extreme learning machine in this model,and the over-fitting phenomenan in traditional extreme learning machine are overcome efficaciously.On the basis of choosing the relative change value of water pressure as characteristic parameter,the optimized extreme learning machine model is applied to leakage fault diagnosis experiment of water supply network,and experimental results show that the fault diagnosis speed and precision of optimized extreme learning machine based on artificial bee colony algorithm are better than the other three models.

关 键 词：极限学习机 优化算法 故障诊断 供水管网 人工蜂群算法 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]